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English(EN) Reframing preprocessing selection as model-internal calibration in near-infrared spectroscopy: A large-scale benchmark of operator-adaptive PLS and Ridge models

新的校准框架简化了近红外光谱数据预处理

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种名为算子自适应校准的新框架，用于简化近红外光谱（NIRS）中的光谱预处理方法的选择。该方法将预处理选择直接集成到校准模型中，减少了对成本高昂且耗时的外部流程搜索的依赖。新模型通过生成可追溯的算子选择并保留可解释的系数，提供了更快、更稳健且可审计的 NIRS 方法开发。 AI

影响 为近红外光谱法（NIRS）中的方法开发提供了一种更有效且可审计的方法，可能影响依赖光谱分析的领域。

排序理由 该集群包含一篇详细介绍新方法和基准测试结果的学术论文。

在 arXiv cs.LG 阅读 →

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新的校准框架简化了近红外光谱数据预处理

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Denis Cornet ·

    将预处理选择重构为近红外光谱中的模型内部校准：算子自适应 PLS 和 Ridge 模型的大规模基准测试

    Near-infrared spectroscopy (NIRS) is rapid and non-destructive, but reliable calibration still depends heavily on spectral preprocessing. In routine practice, preprocessing is often selected by large external pipeline searches that are costly, unstable on small calibration sets, …

  2. arXiv stat.ML TIER_1 English(EN) · Gregory Beurier, Robin Reiter, Camille No\^us, Lauriane Rouan, Denis Cornet ·

    将预处理选择重构为近红外光谱中的模型内部校准：大规模算子自适应 PLS 和 Ridge 模型基准测试

    arXiv:2605.13587v1 Announce Type: new Abstract: Near-infrared spectroscopy (NIRS) is rapid and non-destructive, but reliable calibration still depends heavily on spectral preprocessing. In routine practice, preprocessing is often selected by large external pipeline searches that …

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